For the coarse-to-fine approach,
we let the finer level aggregated correlation map \(\mathcal {A}_p\)  to be guided by the aggregated correlation map of previous (or deeper) levels \(\mathcal {A}_{p+1}\) . Concretely, aggregated correlation map \(\mathcal {A}_{p+1}\)  is up-sampled, which is denoted as \(\mathrm {up}(\mathcal {A}_{p+1})\) , and added to next level's correlation map \(\mathcal {A}_p\)  to play as a guidance. This process is repeated until the finest level prior to decoder. This differs from [1]}, [2]}, which independently aggregates each correlation map and fuses later, that at each level, they have to learn the matching details from scratch while we are guided by the previous level scores, which dramatically boosts the performance. The pyramidal process is defined as:
\(\mathcal {A}_p = \mathrm {VTM}(\mathcal {M}_p+\mathrm {up}(\mathcal {A}_{p+1})),\) 
